Environmental analysis, management and modelling require detailed and precise land-use/land-cover discrimination as initial conditions of land surface characteristics. With the ultimate goal of accurate land surface classification analysis, we devised a fully image-based and physically based correction method (the Integrated Radiometric Correction (IRC) method) considering both the atmospheric and the topographic effects simultaneously, using the information deduced from the satellite images and 5 m resolution DEM data. The overall process is carried out in four steps: (i) calculation of the radiance/irradiance relational expression for horizontal surfaces, (ii) devising the radiance/irradiance relational expression for inclined surfaces, (iii) derivation of solar and land geometric parameters from DEM data, as well as the calculation of the topographic correction factor (A-factor) and the atmospheric transmittance functions, and (iv) retrieval of the corrected surface reflectance and radiance. Using Landsat/ETM + satellite data, the performance of the formulated IRC method is evaluated visually and statistically. Visual evaluation of radiometrically corrected images shows significant improvements for each band as well as for various bands composites, while the independence between the corrected surface reflectance and radiance, and the topography (incidence angle (i) or solar illumination (cos i)) is shown by very weak correlation coefficients as compared with non-corrected data.

A decomposition scheme was applied to ALOS/PALSAR data obtained from a fast-growing tree plantation in Sumatra, Indonesia to extract tree stem information and then estimate the forest stand volume. The scattering power decomposition of the polarimetric SAR data was performed both with and without a rotation matrix and compared to the following field-measured forest biometric parameters: tree diameter, tree height and stand volume. The analytical results involving the rotation matrix correlated better than those without the rotation matrix even for natural scattering surfaces within the forests. Our primary finding was that all of the decomposition powers from the rotated matrix correlated significantly to the forest biometric parameters when divided by the total power. The surface scattering ratio of the total power markedly decreased with the forest growth, whereas the canopy and double-bounce scattering ratios increased. The observations of the OPEN ACCESS Remote Sens. 2012, 4 3059 decomposition powers were consistent with the tree growth characteristics. Consequently, we found a significant logarithmic relationship between the decomposition powers and the forest biometric parameters that can potentially be used to estimate the forest stand volume.

CO 2 sequestration of the forests in Oita Prefecture, Japan, was estimated using satellite remote sensing data. First, hybrid classification of the optical LANDSAT ETM+ data was performed using GIS to produce a detailed land cover map. CO 2 sequestration for each forest type was calculated using the sequestration rates per unit area multiplied by the forest areas obtained from the land cover map This results in 3.57 MtCO 2 /yr for coniferous, 0.77 MtCO 2 /yr for deciduous broadleaf, and 2.25 MtCO 2 /yr for evergreen broadleaf, equivalent to a total of 6.60 MtCO 2 /yr for all the forest covers in Oita. Then, two different methodologies were used to improve these estimates by considering tree ages: the Normalized Difference Vegetation Index (NDVI) and the stem volume methods. Calculation using the NDVI method shows the limitation of this method in providing detailed estimations for trees older than 15 years, because of NDVI saturation beyond this age. In the stem volume method, tree ages were deduced from stem volume values obtained by using PALSAR backscattering data. Sequestration based on tree age forest subclasses yields 2.96 MtCO 2 /yr (coniferous) and 0.31 MtCO 2 /yr (deciduous broadleaf forests). These results show the importance of using not only detailed forest types, but also detailed tree age information for more realistic CO 2 sequestration estimates. In so doing, overestimation of the sequestration capacity of forests could be avoided, and the information on the status and location of forest resources could be improved, thereby leading to sounder decision making in sustainable management of forest resources.

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